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Brester, C., Semenkin, E., & others. (2013). Development of adaptive genetic algorithms for neural network models multicriteria design. Вестник СибГАУ, (4), 99. 
Added by: SijanLibrarian (2020-08-06 09:39:38)   Last edited by: SijanLibrarian (2020-08-06 09:41:24)
Resource type: Journal Article
BibTeX citation key: Brester2013
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Categories: Artificial Intelligence, Biological Science, Complexity Science, Computer Science, Data Sciences, Decision Theory, General
Subcategories: Big data, Informatics, Machine learning, Machine recognition
Creators: Brester, others, Semenkin
Collection: Вестник СибГАУ
Views: 43/59
Views index: 14%
Popularity index: 3.5%
Abstract

In this paper modifications of single- and multi-objective genetic algorithms are described and testing results of these approaches are presented. The gist of the algorithms is the use of the self- adaptation idea leading to reducing of the expert significance for the algorithm setting and expanding of GAs’ application capabilities. On the basis of offered methods the program system realizing the technique for neural network models design was developed. The effectiveness of all algorithms was investigated on a set of test problems.


  
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